23,298 research outputs found

    The application of bayesian and frequentist regularization and variable selection methods for the prediction of asthma in later childhood

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    Asthma is a global health problem and among the most common chronic conditions in childhood. Several models were proposed to predict asthma in children, but their reproducibility in external populations was limited and none was developed to predict asthma in adolescence. I conducted a systematic review of asthma predictive models validated in external populations; validation studies showed poorer predictive performances than development studies. I developed predictive models for asthma between 15 and 20 years, using data from the Study Team for Early Life Asthma Research (STELAR) consortium of five UK asthma cohorts. For one of these cohorts, the Ashford study, I developed an questionnaire to collect follow-up information when study subjects were age 20 years. I harmonised 41 variables across the STELAR cohorts, 39 of which were used as candidate predictors to develop predictive models, while the others were used to define asthma at 15–20 years. Asthma at that age was defined as positive responses to ‘current wheezing’ and ‘asthma medications in the last year’.Two of the five STELAR cohorts (development data) were combined to develop predictive models using stepwise regression and frequentist, Bayesian and empirical Bayes regularization models. The remaining cohorts (validation data) were used to assess predictive performance using discrimination and accuracy measures. Analyses were performed in two populations - all children and a subgroup with reported wheezing between two and five years (high-risk population). Sex, eczema, sensitization to house dust mite and doctor’s diagnosis of asthma in early childhood (4-7 years) were identified as asthma predictors at 15-20 years in both populations. Additional predictors in the general population included early wheezing symptoms and parental allergies, while in the high-risk population maternal allergies and pet in the house at one year were important for asthma prediction in adolescence. Sensitivity was higher in the general population, whereas positive predictive value was higher in the high-risk population. Although accuracy was good in both populations, the predictive ability of the models developed was limited.Open Acces

    Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

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    OBJECTIVES: Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN: Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA: Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527 458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES: Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS: Of the 527 458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49 486 children (9.4%) of the population were classified in the \u27At Higher Risk\u27 group using a threshold of predicted risk \u3e2.5%. The observed risk of hospitalisation (5%) for the \u27At Higher Risk\u27 group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS: It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients\u27 likelihood of a preventable, high-cost hospitalisation

    Understanding Health Risks for Adolescents in Protective Custody

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    Children in child welfare protective custody (e.g., foster care) are known to have increased health concerns compared to children not in protective custody. The poor health documented for children in protective custody persists well into adulthood; young adults who emancipate from protective custody report poorer health, lower quality of life, and increased health risk behaviors compared to young adults in the general population. This includes increased mental health concerns, substance use, sexually transmitted infections, unintended pregnancy, and HIV diagnosis. Identifying youth in protective custody with mental health concerns, chronic medical conditions, and increased health risk behaviors while they remain in custody would provide the opportunity to target prevention and intervention efforts to curtail poor health outcomes while youth are still connected to health and social services. This study leveraged linked electronic health records and child welfare administrative records for 351 youth ages 15 and older to identify young people in custody who were experiencing mental health conditions, chronic medical conditions, and health risk behaviors (e.g., substance use, sexual risk). Results indicate that 41.6% of youth have a mental health diagnosis, with depression and behavior disorders most common. Additionally, 41.3% of youth experience chronic medical conditions, primarily allergies, obesity, and vision and hearing concerns. Finally, 39.6% of youth use substances and 37.0% engage in risky sexual behaviors. Predictors of health risks were examined. Those findings indicate that women, those with longer lengths of stay and more times in custody, and those in independent living and conjugate care settings are at greatest risk for mental health conditions, chronic medical conditions, and health risk behaviors. Results suggest a need to ensure that youth remain connected to health and mental health safety nets, with particular attention needed for adolescents in care for longer and/or those placed in non-family style settings. Understanding who is at risk is critical for developing interventions and policies to target youth who are most vulnerable for increased health concerns that can be implemented while they are in custody and are available to receive services

    Avoidable Hospitalizations

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    Georgia Health Policy Center worked to improve health care in eight of the most rural, medically under served states in the country. The Center conducted research and provided strategic planning for eight Southern states: Alabama, Arkansas, Georgia, Louisiana, Mississippi, South Carolina, East Texas and West Virginia

    Epigenome Modifying Tools In Asthma

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    Extracting information from the text of electronic medical records to improve case detection: a systematic review

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    Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)
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